Predictive Financial Risk Monitoring Using Artificial Intelligence, Advanced Machine Learning and Business Strategy Analytics

Abstract

This paper will look to create a conceptual pipeline for combining artificial intelligence and advanced machine learning technology with institutional risk monitoring to manage shortcomings of legacy financial systems. The key equations of traditional risk models are mostly normally distributed and linear, and hence, can be influenced by non-linear shocks in the market, tail risks and high frequency anomalies. This study implements several supervised, unsupervised and deep learning sequence models for different active cognitive layers in real time risk mitigation based on several data feeds related to market, credit and operation. Moreover, to ensure compliance with strict regulations and continuation of fiduciary responsibility in the ever-changing financial landscape, the research underscores the significance of explainable artificial intelligence (XAI), walk-forward cross-validation, and human-in-the-loop override systems.